CN116090799A - Wharf production scheduling method and system for complex bulk grain operation - Google Patents

Wharf production scheduling method and system for complex bulk grain operation Download PDF

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CN116090799A
CN116090799A CN202310374700.6A CN202310374700A CN116090799A CN 116090799 A CN116090799 A CN 116090799A CN 202310374700 A CN202310374700 A CN 202310374700A CN 116090799 A CN116090799 A CN 116090799A
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berth
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王雪琳
石浛锟
鲁东起
周鸿茂
卢宁
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China Waterborne Transport Research Institute
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Abstract

The invention belongs to the technical field of port production operation, and discloses a dock production scheduling method and system for complex bulk grain operation: step 1, constructing a bulk grain wharf multi-resource cooperative scheduling model A by taking port throughput, berth utilization rate, total time of a ship in a port and travel distance of a transport machine as targets; step 2, solving a bulk grain wharf multi-resource cooperative scheduling model A by adopting a particle swarm algorithm based on a Pareto method to obtain an optimal plan of berth scheduling and yard scheduling; and 3, arranging berths and storage yards according to the optimal plan. The invention solves the problems of low precision of bulk grain production scheduling plan, low utilization rate of various resources of wharfs and unreasonable berth selection.

Description

Wharf production scheduling method and system for complex bulk grain operation
Technical Field
The invention belongs to the technical field of port production operation, and particularly relates to a dock production scheduling method and system for complex bulk grain operation.
Background
The bulk grain wharf collaborative optimization scheduling target ensures the smooth execution of a loading and unloading plan, fully utilizes the self conditions of a storage yard and berths, and better utilizes the existing berths. And the convenience of goods entering and exiting the storage yard and loading and unloading operations in the storage yard is considered, and the requirements of loading and unloading efficiency of ships with different priorities are met. Meanwhile, the horizontal conveying distance, loading and unloading and conveying times and the working labor level are reduced. The job route and dispatch plan should also meet safety requirements.
Along with the continuous acceleration of the construction and development of the port industry, the throughput demand of bulk grain wharfs is increased, and how to reasonably and efficiently formulate a bulk grain production scheduling plan, so that the improvement of port efficiency and comprehensive competitiveness become important problems facing port enterprises. Bulk grain wharf has the characteristics of various goods, huge quantity, complex operation flow and the like, and the scheduling efficiency is a key for influencing the economic benefit of the whole wharf. At present, most of the dispatching methods of bulk grain wharfs adopt traditional dispatching methods based on manual experience. The scheduling method is mainly based on scheduling experience, and according to actual conditions of the site, berths, yards and the like are manually specified. The traditional scheduling method is suitable for the condition of relatively less throughput of wharf, but as the number of ships is increased and the variety of goods is increased, the scheduling accuracy of the traditional scheduling method is reduced, so that the problems of low resource utilization rate, unreasonable berth selection and the like are caused, and the scheduling purely relying on experience can not be suitable for complex dynamic changes of wharf. How to fully utilize various resources of the wharf to achieve the maximum dispatching efficiency becomes a concern of the bulk grain wharf. By adopting a high-efficiency intelligent scheduling mode, the port informatization level is enhanced, and the improvement of the loading and unloading operation efficiency becomes the key point of further development of ports in China. With the continuous increase of competition between port enterprises, providing high-quality service and intelligent functions has become an important means of competition between port enterprises. Therefore, when the port scheduling scheme is studied, in addition to the operation cost and loading and unloading efficiency of the port, various factors such as lifting and unloading efficiency of ships, clients and transportation companies should be considered. At present, the scheduling research on bulk grain wharfs is still insufficient, and further analysis and research are needed.
Disclosure of Invention
Aiming at the technical problems, the invention provides a wharf production scheduling method and a wharf production scheduling system aiming at complex bulk grain operation.
In a first aspect, the present invention provides a dock production scheduling method for complex bulk grain operations, the method comprising:
step 1, taking whether a ship is berthed in a planning period, whether goods are unloaded to a storage yard in the planning period, whether the goods are loaded to the ship in the planning period, the horizontal distance between the berth and the storage yard, the ship operation waiting time, the ship operation time on the berth, the latest departure time of the ship, the earliest entering anchor waiting time of the ship, the berth length and the cargo carrying capacity of the ship as scheduling objects, and constructing a bulk grain wharf multi-resource cooperative scheduling model A by taking port throughput, berth utilization rate, total ship time and transport machinery driving distance as targets;
step 2, solving a bulk grain wharf multi-resource cooperative scheduling model A by adopting a particle swarm algorithm based on a Pareto method to obtain an optimal plan of berth scheduling and yard scheduling;
and 3, arranging berths and storage yards according to the optimal plan.
Specifically, the calculation formula of the bulk grain wharf multi-resource cooperative scheduling model A is as follows:
Figure SMS_1
，
wherein f1 represents port throughput, f2 represents port berth utilization rate, f3 represents total time of the ship in the port, f4 represents travel distance of the transportation machinery, and alpha 1, alpha 2, alpha 3 and alpha 4 are coefficient weights.
Specifically, the expression for port throughput is as follows:
Figure SMS_2
，
the expression of the harbour berth utilization is as follows:
Figure SMS_3
，
the expression of the total time of the ship in port is as follows:
Figure SMS_4
，
the expression of the travel distance of the transport machine is as follows:
Figure SMS_5
，
wherein I is the number of the ship, J is the number of berths, J is the total number of berths, I is the total number of the ship, and P i，j A value of 0 or 1, 1 representing that the ship i is berthed on the berth in the planning period, C i，s The value of 0 or 1 is taken, 1 is taken to represent that the goods loaded by the ship i are unloaded to the storage yard s, J in the planning period i，s A value of 0 or 1, 1 representing loading of the cargo of the yard s to the vessel in the planning period, l j,s TW is the horizontal distance between berth j and yard s i -TI i For the job waiting time of vessel i, WI i /V j For the operating time of the ship i at the berth j, t max G is the latest departure time of all ships min For the time of earliest entry of all vessels into the anchor, m j For the length of berth j, including the safe distance between vessels, d i,z For the cargo hold z load, t of the vessel i i Represents departure time g of ship i completing loading and unloading operations i Indicating that the ship i completes the loading and unloading operationDeparture time of m j Representing the length of the berth j.
Specifically, a particle swarm algorithm based on a Pareto method is adopted to solve a bulk grain wharf multi-resource cooperative scheduling model A, and the method comprises the following steps:
step 21, initializing a particle swarm, and generating constraint conditions for berths and storage yards;
step 22, calculating an adaptation value of each particle according to the adaptation function, and storing an individual extremum and a global extremum found by each particle;
step 23, constructing a pareto optimal solution set;
step 24, under the limitation of the constraint matrix, updating the position and the speed of the particles, and carrying out random variation operation on the particles;
step 25, judging whether the constraint condition is satisfied, if not, returning to step 24 to update the position and speed of the particles again; if yes, updating the speed and the position, and updating the optimal solution set;
step 26, judging whether the preset iteration times are reached, and if not, returning to the step 22; and if so, outputting the optimal solution set.
Specifically, the solving formula of the particle swarm algorithm is:
Figure SMS_6
，
Figure SMS_7
，
wherein b1 and b2 are random numbers uniformly distributed, and take on values of [0,1 ]]，
Figure SMS_8
The position vector representing the ith particle, m represents the iteration algebra, i is the index of the particle, i=1, 2, …, n, e1 and e2 represent the learning factors, W ij ={W i1 ,W i2 ,…,W ij The optimal solution of population searching is V ij ={V i1 ,V i2 ,…,V ij Is the global optimum position, U tj ={U t1 ,U t2 ,…,U tj And } is a velocity variable.
In a second aspect, the present invention also provides a dock production scheduling system for complex bulk grain operations, the system comprising:
the model construction module is used for constructing a bulk grain wharf multi-resource co-scheduling model A by taking whether a ship is berthed in a planning period, whether goods are unloaded to a storage yard in the planning period, whether the goods are loaded to the ship in the planning period, the horizontal distance between the berth and the storage yard, the ship operation waiting time, the ship operation time on the berth, the latest departure time of the ship, the earliest entry anchor waiting time of the ship, the berth length and the cargo carrying capacity of the ship as scheduling objects and taking port throughput, berth utilization rate, the total time of the ship in the port and the running distance of a transport machine as targets;
the optimal plan determining module is used for solving the multi-resource cooperative scheduling model A of the bulk grain wharf by adopting a particle swarm algorithm based on a Pareto method to obtain an optimal plan of berth scheduling and yard scheduling;
and the scheduling execution module is used for scheduling berths and storage yards according to the optimal plan.
Specifically, the calculation formula of the bulk grain wharf multi-resource cooperative scheduling model A is as follows:
Figure SMS_9
，
wherein f1 represents port throughput, f2 represents port berth utilization rate, f3 represents total time of the ship in the port, f4 represents travel distance of the transportation machinery, and alpha 1, alpha 2, alpha 3 and alpha 4 are coefficient weights.
Specifically, the expression for port throughput is as follows:
Figure SMS_10
，
the expression of the harbour berth utilization is as follows:
Figure SMS_11
，
the expression of the total time of the ship in port is as follows:
Figure SMS_12
，
the expression of the travel distance of the transport machine is as follows:
Figure SMS_13
，
wherein I is the number of the ship, J is the number of berths, J is the total number of berths, I is the total number of the ship, and P i，j A value of 0 or 1, 1 representing that the ship i is berthed on the berth in the planning period, C i，s The value of 0 or 1 is taken, 1 is taken to represent that the goods loaded by the ship i are unloaded to the storage yard s, J in the planning period i，s A value of 0 or 1, 1 representing loading of the cargo of the yard s to the vessel in the planning period, l j,s TW is the horizontal distance between berth j and yard s i -TI i For the job waiting time of vessel i, WI i /V j For the operating time of the ship i at the berth j, t max G is the latest departure time of all ships min For the time of earliest entry of all vessels into the anchor, m j For the length of berth j, including the safe distance between vessels, d i,z For the cargo hold z load, t of the vessel i i Represents departure time g of ship i completing loading and unloading operations i Represents departure time, m of ship i completing loading and unloading operation j Representing the length of the berth j.
Specifically, a particle swarm algorithm based on a Pareto method is adopted to solve a bulk grain wharf multi-resource cooperative scheduling model A, and the method comprises the following steps:
step 21, initializing a particle swarm, and generating constraint conditions for berths and storage yards;
step 22, calculating an adaptation value of each particle according to the adaptation function, and storing an individual extremum and a global extremum found by each particle;
step 23, constructing a pareto optimal solution set;
step 24, under the limitation of the constraint matrix, updating the position and the speed of the particles, and carrying out random variation operation on the particles;
step 25, judging whether the constraint condition is satisfied, if not, returning to step 24 to update the position and speed of the particles again; if yes, updating the speed and the position, and updating the optimal solution set;
step 26, judging whether the preset iteration times are reached, and if not, returning to the step 22; and if so, outputting the optimal solution set.
Specifically, the solving formula of the particle swarm algorithm is:
Figure SMS_14
，
Figure SMS_15
，
wherein b1 and b2 are random numbers uniformly distributed, and take on values of [0,1 ]]，
Figure SMS_16
The position vector representing the ith particle, m represents the iteration algebra, i is the index of the particle, i=1, 2, …, n, e1 and e2 represent the learning factors, W ij ={W i1 ,W i2 ,…,W ij The optimal solution of population searching is V ij ={V i1 ,V i2 ,…,V ij Is the global optimum position, U tj ={U t1 ,U t2 ,…,U tj And } is a velocity variable.
Compared with the prior art, the invention has the beneficial effects that at least the following steps are adopted:
1. the utilization rate of the berths and the storage yards is improved, the operation requirements of the storage yards and the convenience of bulk grain goods entering and exiting the storage yards and production operation are fully considered, the transportation driving distance is reduced, and the scheduling mode is optimized;
2. the equipment resources of the bulk grain wharf are reasonably and effectively arranged, the requirements of loading and unloading efficiencies of ships with different priorities are considered, and the dispatching production operation efficiency of the bulk grain wharf is improved;
3. combining multiple influencing factors such as berths, storage yards, processes and the like, and various types of bulk grain goods, different packaging modes, different storage conditions and different loading and unloading modes, and constructing a dispatching system suitable for bulk grain transportation;
4. the port production flow and scheduling are combined together through a scientific calculation method, and an efficient scheduling scheme is formulated by combining the actual production condition;
5. the workload of dock dispatcher is reduced, and the operation flow is optimized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a dock production scheduling method for complex bulk operations of the present invention;
fig. 2 is a schematic structural diagram of a dock production scheduling system for complex bulk operations according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be apparent that the particular embodiments described herein are merely illustrative of the present invention and are some, but not all embodiments of the present invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on embodiments of the present invention, are within the scope of the present invention.
It should be noted that, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is only for descriptive purposes, and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Fig. 1 is a flowchart of an embodiment of a dock production scheduling method for complex bulk grain operations according to the present invention, where the flowchart specifically includes:
step 1, taking whether a ship is berthed in a planning period, whether goods are unloaded to a storage yard in the planning period, whether the goods are loaded to the ship in the planning period, the horizontal distance between the berth and the storage yard, the ship operation waiting time, the ship operation time on the berth, the latest departure time of the ship, the earliest entering anchor waiting time of the ship, the berth length and the carrying capacity of the ship as scheduling objects, and constructing a bulk grain wharf multi-resource cooperative scheduling model A by taking port throughput, berth utilization rate, total ship time and transport machinery driving distance as targets.
According to an embodiment of the present invention, the model assumption of the bulk grain terminal multi-resource co-scheduling model a is:
1) At any moment, a certain berth can only dock one ship, and at the same time, one ship can only dock one berth;
2) When the ship selects berthing, the length of the ship is smaller than that of the berthing, and the draft of the ship is smaller than that of the berthing;
3) Each stacking position only allows stacking one cargo, and if the weight of the cargo is higher than the maximum capacity of a storage yard, the cargo needs to be split according to the capacity of the storage yard;
4) The same type of machines in the bulk grain wharf have the same operation efficiency, and the condition that loading and unloading machines wait for transporting machines does not exist;
5) All ships loaded with the same cargo should lean against according to the principle of first come first served;
6) After the ship is berthed, the loading and unloading machinery starts to operate, and after the operation is finished, the ship leaves the port without the termination of midway operation;
7) The total amount of the port yard, port operators and operation machinery meet the requirements;
8) Landing ship information and known cargo loading information within the planned days;
9) The maximum capacity of the bulk grain yard is larger than the maximum cargo capacity of the ship.
Specifically, the calculation formula of the bulk grain wharf multi-resource cooperative scheduling model A is as follows:
Figure SMS_17
，
wherein f1 represents port throughput, f2 represents port berth utilization rate, f3 represents total time of the ship in the port, f4 represents travel distance of the transportation machinery, and alpha 1, alpha 2, alpha 3 and alpha 4 are coefficient weights.
Specifically, the expression for port throughput is as follows:
Figure SMS_18
，
the expression of the harbour berth utilization is as follows:
Figure SMS_19
，
the expression of the total time of the ship in port is as follows:
Figure SMS_20
，
the expression of the travel distance of the transport machine is as follows:
Figure SMS_21
，
wherein I is the number of the ship, J is the number of berths, J is the total number of berths, I is the total number of the ship, and P i，j A value of 0 or 1, 1 representing that the ship i is berthed on the berth in the planning period, C i，s The value of 0 or 1 is 1, and 1 represents that the ship is moved in the planning periodi unloading the loaded goods to a yard s, J i，s A value of 0 or 1, 1 representing loading of the cargo of the yard s to the vessel in the planning period, l j,s TW is the horizontal distance between berth j and yard s i -TI i For the job waiting time of vessel i, WI i /V j For the operating time of the ship i at the berth j, t max G is the latest departure time of all ships min For the time of earliest entry of all vessels into the anchor, m j For the length of berth j, including the safe distance between vessels, d i,z For the cargo hold z load, t of the vessel i i Represents departure time g of ship i completing loading and unloading operations i Represents departure time, m of ship i completing loading and unloading operation j Representing the length of the berth j.
Through reasonable arrangement of berths and yards, the residence time of ships at ports can be reduced, subsequent ships can be enabled to resist port operation as soon as possible, and the overall operation efficiency of ports is improved.
And 2, solving the bulk grain wharf multi-resource cooperative scheduling model A by adopting a particle swarm algorithm based on a Pareto method to obtain an optimal plan of berth scheduling and yard scheduling.
Specifically, a particle swarm algorithm based on a Pareto method is adopted to solve a bulk grain wharf multi-resource cooperative scheduling model A, and the method comprises the following steps:
step 21, initializing a particle swarm, and generating constraint conditions for berths and storage yards;
step 22, calculating an adaptation value of each particle according to the adaptation function, and storing an individual extremum and a global extremum found by each particle;
step 23, constructing a pareto optimal solution set;
step 24, under the limitation of the constraint matrix, updating the position and the speed of the particles, and carrying out random variation operation on the particles;
step 25, judging whether the constraint condition is satisfied, if not, returning to step 24 to update the position and speed of the particles again; if yes, updating the speed and the position, and updating the optimal solution set;
step 26, judging whether the preset iteration times are reached, and if not, returning to the step 22; and if so, outputting the optimal solution set.
Checking whether the pareto optimal solution set generated in the step 26 of the population meets the preset iteration termination condition, if so, outputting the optimal solution set output in the step 25 as an optimizing result, otherwise, returning to the step 22 to continue iteration.
The particle swarm algorithm can be described as a population k= { X in an h-dimensional space where there are n particles that represent the solution of the problem 1 ,X 2 ,…,X N And n is a vector point of the ith particle in the h-dimensional solution space. The optimum point of the ith particle search is denoted by V, W ij ={W i1 ,W i2 ,…,W ij The optimal solution of population searching is V ij ={V i1 ,V i2 ,…,V ij And is the global optimum. In addition to the optimum position, the particles have a velocity variable, which can be measured by U tj Representation, wherein U t ={U t1 ,U t2 ,…,U tj },i=1,2,…,n。
Specifically, the solving formula of the particle swarm algorithm is:
Figure SMS_22
，
Figure SMS_23
，
wherein b1 and b2 are random numbers uniformly distributed, and take on values of [0,1 ]]，
Figure SMS_24
The position vector representing the ith particle, m represents the iteration algebra, i is the index of the particle, i=1, 2, …, n, e1 and e2 represent the learning factors, W ij ={W i1 ,W i2 ,…,W ij The optimal solution of population searching is V ij ={V i1 ,V i2 ,…,V ij Is the global optimum position, U tj ={U t1 ,U t2 ,…,U tj And } is a velocity variable.
The scheduling problem of bulk grain wharf is a multi-objective optimization problem, the particle swarm algorithm based on the Pareto method is adopted, the algorithm is simple in form, the parameter adjusting mechanism is flexible, and the convergence speed is high, so that the particle swarm optimization method is one of widely adopted algorithms. The invention divides the scheduling problem in particle coding design into two parts of berth scheduling and yard scheduling, respectively codes the berth scheduling and yard scheduling, processes constraint conditions, establishes a bulk grain wharf as a scheduling body, analyzes the berth and yard constraint conditions to obtain a constraint matrix, obtains the fitness value of particles, obtains the optimal solution set of particle swarm, updates the solution set by updating the speed and the position of the particles, and finally obtains the output optimal solution set.
And 3, arranging berths and storage yards according to the optimal plan.
Fig. 2 is a schematic structural view of an embodiment of a dock production scheduling system for complex bulk operations provided by the present invention. As shown in fig. 2, the system includes:
the model construction module is used for constructing a bulk grain wharf multi-resource co-scheduling model A by taking whether a ship is berthed in a planning period, whether goods are unloaded to a storage yard in the planning period, whether the goods are loaded to the ship in the planning period, the horizontal distance between the berth and the storage yard, the ship operation waiting time, the ship operation time on the berth, the latest departure time of the ship, the earliest entry anchor waiting time of the ship, the berth length and the cargo carrying capacity of the ship as scheduling objects and taking port throughput, berth utilization rate, the total time of the ship in the port and the running distance of a transport machine as targets;
the optimal plan determining module is used for solving the multi-resource cooperative scheduling model A of the bulk grain wharf by adopting a particle swarm algorithm based on a Pareto method to obtain an optimal plan of berth scheduling and yard scheduling;
and the scheduling execution module is used for scheduling berths and storage yards according to the optimal plan.
Specifically, the calculation formula of the bulk grain wharf multi-resource cooperative scheduling model A is as follows:
Figure SMS_25
，
wherein f1 represents port throughput, f2 represents port berth utilization rate, f3 represents total time of the ship in the port, f4 represents travel distance of the transportation machinery, and alpha 1, alpha 2, alpha 3 and alpha 4 are coefficient weights.
Specifically, the expression for port throughput is as follows:
Figure SMS_26
，
the expression of the harbour berth utilization is as follows:
Figure SMS_27
，
the expression of the total time of the ship in port is as follows:
Figure SMS_28
，
the expression of the travel distance of the transport machine is as follows:
Figure SMS_29
，
wherein I is the number of the ship, J is the number of berths, J is the total number of berths, I is the total number of the ship, and P i，j A value of 0 or 1, 1 representing that the ship i is berthed on the berth in the planning period, C i，s The value of 0 or 1 is taken, 1 is taken to represent that the goods loaded by the ship i are unloaded to the storage yard s, J in the planning period i，s A value of 0 or 1, 1 representing loading of the cargo of the yard s to the vessel in the planning period, l j,s TW is the horizontal distance between berth j and yard s i -TI i For the job waiting time of vessel i, WI i /V j For the operating time of the ship i at the berth j, t max G is the latest departure time of all ships min For the time of earliest entry of all vessels into the anchor, m j For the length of berth j, including the safe distance between vessels, d i,z For the cargo hold z load, t of the vessel i i Separation indicating completion of loading and unloading operation by ship iHarbor time, g i Represents departure time, m of ship i completing loading and unloading operation j Representing the length of the berth j.
Specifically, a particle swarm algorithm based on a Pareto method is adopted to solve a bulk grain wharf multi-resource cooperative scheduling model A, and the method comprises the following steps:
step 21, initializing a particle swarm, and generating constraint conditions for berths and storage yards;
step 22, calculating an adaptation value of each particle according to the adaptation function, and storing an individual extremum and a global extremum found by each particle;
step 23, constructing a pareto optimal solution set;
step 24, under the limitation of the constraint matrix, updating the position and the speed of the particles, and carrying out random variation operation on the particles;
step 25, judging whether the constraint condition is satisfied, if not, returning to step 24 to update the position and speed of the particles again; if yes, updating the speed and the position, and updating the optimal solution set;
step 26, judging whether the preset iteration times are reached, and if not, returning to the step 22; and if so, outputting the optimal solution set.
Specifically, the solving formula of the particle swarm algorithm is:
Figure SMS_30
，
Figure SMS_31
，
wherein b1 and b2 are random numbers uniformly distributed, and take on values of [0,1 ]]，
Figure SMS_32
The position vector representing the ith particle, m represents the iteration algebra, i is the index of the particle, i=1, 2, …, n, e1 and e2 represent the learning factors, W ij ={W i1 ,W i2 ,…,W ij The optimal solution of population searching is V ij ={V i1 ,V i2 ,…,V ij Is the global optimum position, U tj ={U t1 ,U t2 ,…,U tj And } is a velocity variable.
The foregoing examples have shown only the preferred embodiments of the invention, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.

Claims (10)

1. A dock production scheduling method for complex bulk grain operation is characterized by comprising the following steps:
step 1, taking whether a ship is berthed in a planning period, whether goods are unloaded to a storage yard in the planning period, whether the goods are loaded to the ship in the planning period, the horizontal distance between the berth and the storage yard, the ship operation waiting time, the ship operation time on the berth, the latest departure time of the ship, the earliest entering anchor waiting time of the ship, the berth length and the cargo carrying capacity of the ship as scheduling objects, and constructing a bulk grain wharf multi-resource cooperative scheduling model A by taking port throughput, berth utilization rate, total ship time and transport machinery driving distance as targets;
step 2, solving the bulk grain wharf multi-resource co-scheduling model A by adopting a particle swarm algorithm based on a Pareto method to obtain an optimal plan of berth scheduling and yard scheduling;
and 3, arranging berths and storage yards according to the optimal plan.
2. The dock production scheduling method for complex bulk grain operations according to claim 1, wherein the calculation formula of the bulk grain dock multi-resource co-scheduling model a is as follows:
Figure QLYQS_1
，
wherein f1 represents port throughput, f2 represents port berth utilization rate, f3 represents total time of the ship in the port, f4 represents travel distance of the transportation machinery, and alpha 1, alpha 2, alpha 3 and alpha 4 are coefficient weights.
3. The terminal production scheduling method for complex bulk grain operations of claim 2, wherein the expression of the port throughput is as follows:
Figure QLYQS_2
，
the expression of the port berth utilization rate is as follows:
Figure QLYQS_3
，
the expression of the total time of the ship in port is as follows:
Figure QLYQS_4
，
the expression of the travel distance of the transport machine is as follows:
Figure QLYQS_5
，
wherein I is the number of the ship, J is the number of berths, J is the total number of berths, I is the total number of the ship, and P i，j A value of 0 or 1, 1 representing that the ship i is berthed on the berth in the planning period, C i，s The value of 0 or 1 is taken, 1 is taken to represent that the goods loaded by the ship i are unloaded to the storage yard s, J in the planning period i，s A value of 0 or 1, 1 representing loading of the cargo of the yard s to the vessel in the planning period, l j,s TW is the horizontal distance between berth j and yard s i -TI i For the job waiting time of vessel i, WI i /V j For the operating time of the ship i at the berth j, t max For the latest departure time of all ships,g min for the time of earliest entry of all vessels into the anchor, m j For the length of berth j, including the safe distance between vessels, d i,z For the cargo hold z load, t of the vessel i i Represents departure time g of ship i completing loading and unloading operations i Represents departure time, m of ship i completing loading and unloading operation j Representing the length of the berth j.
4. The dock production scheduling method for complex bulk grain operations of claim 1, wherein solving the bulk grain dock multi-resource co-scheduling model a by adopting a particle swarm algorithm based on a Pareto method comprises:
step 21, initializing a particle swarm, and generating constraint conditions for berths and storage yards;
step 22, calculating an adaptation value of each particle according to the adaptation function, and storing an individual extremum and a global extremum found by each particle;
step 23, constructing a pareto optimal solution set;
step 24, under the limitation of the constraint matrix, updating the position and the speed of the particles, and carrying out random variation operation on the particles;
step 25, judging whether the constraint condition is satisfied, if not, returning to the step 24 to update the position and speed of the particles again; if yes, updating the speed and the position, and updating the optimal solution set;
step 26, judging whether the preset iteration times are reached, and if not, returning to the step 22; and if so, outputting the optimal solution set.
5. The dock production scheduling method for complex bulk grain operations of claim 4, wherein the solution formula of the particle swarm algorithm is:
Figure QLYQS_6
，
Figure QLYQS_7
，
wherein b1 and b2 are random numbers uniformly distributed, and take on values of [0,1 ]]，
Figure QLYQS_8
The position vector representing the ith particle, m represents the iteration algebra, i is the index of the particle, i=1, 2, …, n, e1 and e2 represent the learning factors, W ij ={W i1 ,W i2 ,…,W ij The optimal solution of population searching is V ij ={V i1 ,V i2 ,…,V ij Is the global optimum position, U tj ={U t1 ,U t2 ,…,U tj And } is a velocity variable.
6. A terminal production scheduling system for complex bulk grain operations, comprising:
the model construction module is used for constructing a bulk grain wharf multi-resource co-scheduling model A by taking whether a ship is berthed in a planning period, whether goods are unloaded to a storage yard in the planning period, whether the goods are loaded to the ship in the planning period, the horizontal distance between the berth and the storage yard, the ship operation waiting time, the ship operation time on the berth, the latest departure time of the ship, the earliest entry anchor waiting time of the ship, the berth length and the cargo carrying capacity of the ship as scheduling objects and taking port throughput, berth utilization rate, the total time of the ship in the port and the running distance of a transport machine as targets;
the optimal plan determining module is used for solving the bulk grain wharf multi-resource cooperative scheduling model A by adopting a particle swarm algorithm based on a Pareto method to obtain an optimal plan of berth scheduling and yard scheduling;
and the scheduling execution module is used for scheduling berths and storage yards according to the optimal plan.
7. The terminal production scheduling system for complex bulk grain operations of claim 6, wherein the bulk grain terminal multi-resource co-scheduling model a has the following calculation formula:
Figure QLYQS_9
，
wherein f1 represents port throughput, f2 represents port berth utilization rate, f3 represents total time of the ship in the port, f4 represents travel distance of the transportation machinery, and alpha 1, alpha 2, alpha 3 and alpha 4 are coefficient weights.
8. The terminal production scheduling system for complex bulk operations of claim 7, wherein the expression for port throughput is as follows:
Figure QLYQS_10
，/>
the expression of the port berth utilization rate is as follows:
Figure QLYQS_11
，
the expression of the total time of the ship in port is as follows:
Figure QLYQS_12
，
the expression of the travel distance of the transport machine is as follows:
Figure QLYQS_13
，
wherein I is the number of the ship, J is the number of berths, J is the total number of berths, I is the total number of the ship, and P i，j A value of 0 or 1, 1 representing that the ship i is berthed on the berth in the planning period, C i，s The value of 0 or 1 is taken, 1 is taken to represent that the goods loaded by the ship i are unloaded to the storage yard s, J in the planning period i，s A value of 0 or 1, 1 representing loading of the cargo of the yard s to the vessel in the planning period, l j,s TW is the horizontal distance between berth j and yard s i -TI i For the job waiting time of vessel i, WI i /V j For the operating time of the ship i at the berth j, t max G is the latest departure time of all ships min For the time of earliest entry of all vessels into the anchor, m j For the length of berth j, including the safe distance between vessels, d i,z For the cargo hold z load, t of the vessel i i Represents departure time g of ship i completing loading and unloading operations i Represents departure time, m of ship i completing loading and unloading operation j Representing the length of the berth j.
9. The dock production scheduling system for complex bulk grain operations of claim 6, wherein solving the bulk grain dock multi-resource co-scheduling model a using a Pareto-based particle swarm algorithm comprises:
step 21, initializing a particle swarm, and generating constraint conditions for berths and storage yards;
step 22, calculating an adaptation value of each particle according to the adaptation function, and storing an individual extremum and a global extremum found by each particle;
step 23, constructing a pareto optimal solution set;
step 24, under the limitation of the constraint matrix, updating the position and the speed of the particles, and carrying out random variation operation on the particles;
step 25, judging whether the constraint condition is satisfied, if not, returning to the step 24 to update the position and speed of the particles again; if yes, updating the speed and the position, and updating the optimal solution set;
step 26, judging whether the preset iteration times are reached, and if not, returning to the step 22; and if so, outputting the optimal solution set.
10. The terminal production scheduling system for complex bulk grain operations of claim 9, wherein the solution formula of the particle swarm algorithm is:
Figure QLYQS_14
，
Figure QLYQS_15
，
wherein b1 and b2 are random numbers uniformly distributed, and take on values of [0,1 ]]，
Figure QLYQS_16
The position vector representing the ith particle, m represents the iteration algebra, i is the index of the particle, i=1, 2, …, n, e1 and e2 represent the learning factors, W ij ={W i1 ,W i2 ,…,W ij The optimal solution of population searching is V ij ={V i1 ,V i2 ,…,V ij Is the global optimum position, U tj ={U t1 ,U t2 ,…,U tj And } is a velocity variable. />
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